Approximation of a Microbiome Composition Shift by a Change in a Single Balance Between Two Groups of Taxa

Effect of Lactobacillus reuteri NCIMB 30351 drops on symptoms of infantile functional gastrointestinal disorders and gut microbiota in early infants: Results from a randomized, placebo-controlled clinical trial

Infantile functional gastrointestinal disorders, such as colic, constipation, diarrhea, and gastroesophageal reflux (regurgitation), often occur in early infancy and, representing one of the causes of significant parental anxiety, lead to a significant strain on the healthcare resources. In this study, we aimed to evaluate the effects of Lactobacillus reuteri drops (L. reuteri NCIMB 30351) on the symptoms of infantile colic, constipation, diarrhea, and gastroesophageal reflux, as well as on the levels of intestinal microbiota in full-term newborns during the first months of life. A randomized, placebo-controlled, single-masked (blinded), post-marketing clinical study was conducted in two clinical units-Children's City Clinical Hospital of Moscow and Medical Center "St. Andrew's Hospitals-NEBOLIT" from March 2020 to May 2022 in 90 infants aged from 1 to 4 months (mean age (± SD) 12.3 ± 5.09 weeks; 53.3% females, 46.7% males). Patients with colic, regurgitation (single symptom or combination of several symptoms), and constipation or diarrhea were randomly allocated in two parallel arms to receive either 5 drops (2 × 108 colony forming unit) of L. reuteri NCIMB 30351 (n = 60) or masked placebo (n = 30) for 25 consecutive days. Two treatment arms had equal numbers of patients with constipation and diarrhea (n = 30 each). Daily crying times and their duration, evacuations, and regurgitations were recorded in a structured diary. The levels of gut microbiota were analyzed by deep sequencing of bacterial 16S rRNA gene. Infants with colic receiving supplementary L. reuteri NCIMB 30351 for 25 days had significant reduction in the numbers of colic (change from baseline - 6.3 (7.34) vs - 3.0 (7.29) in placebo, P < 0.05) and numbers of crying cases and mean duration of crying (decrease from baseline - 144 (70.7) minutes, lower in the diarrhea subgroup than in constipation infants, compared with - 80 (58.9) in placebo, P < 0.0001), as well as regurgitation numbers (decreased by - 4.8 (2.49) with L. reuteri vs - 3 (7.74) with placebo). We also observed increased numbers of evacuations in infants with constipation (L. reuteri 2.2 (2.4) vs 0.9 (1.06) in placebo, P < 0.05). There was a remarkable reduction of evacuations in infants with diarrhea, while not statistically significant. The analysis of bacterial 16S rRNA gene in the collected samples showed that L. reuteri positively influences the proportions of prevalent species, while it negatively affects both conditionally pathogenic and commensal microbes. Additional in vitro test for formation of Clostridium colonies in the presence of the probiotic demonstrated that L. reuteri effectively inhibits the growth of pathogenic Clostridium species. No adverse events were reported in this study.   Conclusion: The uptake of L. reuteri NCIMB 30351 leads to a significant reduction in the number of regurgitations, feeding-induced constipations, and diarrhea as well as mean daily numbers of crying and crying duration in infants during the first months of life. Our results suggest that L. reuteri NCIMB 30351 represents a safe and effective treatment for colic in newborns.  Trial registration: ClinicalTrials.gov : NCT04262648. What is Known: • Infantile functional gastrointestinal disorders, such as colic, constipation, diarrhea, and gastroesophageal reflux (regurgitation), often occur in early infancy and, represent one of the causes of significant parental anxiety. • A number of studies have shown that both the composition and diversity of the intestinal microbiota play important roles in the development and function of the gastrointestinal tract. What is New: • The uptake of L. reuteri NCIMB 30351 leads to a significant reduction in the number of regurgitations, feeding-induced constipations, and diarrhea as well as mean daily numbers of crying and crying duration in infants during the first months of life. • L. reuteri positively influences the proportions of prevalent species, while it negatively affects both conditionally pathogenic and commensal microbes in gut microbiota.

Stability of human gut microbiome: Comparison of ecological modelling and observational approaches

The gut microbiome plays a pivotal role in the human body, and perturbations in its composition have been linked to various disorders. Stability is an essential property of a healthy human gut microbiome, which allows it to maintain its functional richness under the external influences. This property has been explored through two distinct methodologies - mathematical modelling based on ecological principles and statistical analysis drawn from observations in interventional studies. Here we conducted a meta-analysis aimed to compare the two approaches utilising the data from 9 interventional and time series studies encompassing 3512 gut microbiome profiles obtained via 16S rRNA gene sequencing. By employing the previously published compositional Lotka-Volterra method, we modelled the dynamics of the microbial community and evaluated ecological stability measures. These measures were compared to those based on observed microbiome changes. There was a substantial correlation between the outcomes of the two approaches. Particularly, local stability assessed within the ecological paradigm was positively correlated with observational stability measures accounting for the compositional nature of microbiome data. Additionally, we were able to reproduce the previously reported inverse relationship between the community's robustness to microorganism loss and local stability, attributed to the distinct impacts of coefficient characterising the network decomposition on these two stability assessments. Our findings demonstrate harmonisation between the ecological and observational approaches to microbiome analysis, advancing the understanding of healthy gut microbiome concept. This paves the way to develop efficient microbiome-targeting interventions for disease prevention and treatment.

Gut Microbiome in Post-COVID-19 Patients Is Linked to Immune and Cardiovascular Health Status but Not COVID-19 Severity

The composition of the gut microbiome stores the imprints of prior infections and other impacts. COVID-19 can cause changes in inflammatory status that persist for a considerable time after infection ends. As the gut microbiome is closely associated with immunity and inflammation, the infection severity might be linked to its community structure dynamics. Using 16S rRNA sequencing of stool samples, we investigated the microbiome three months after the end of the disease/infection or SARS-CoV-2 contact in 178 post-COVID-19 patients and those who contacted SARS-CoV-2 but were not infected. The cohort included 3 groups: asymptomatic subjects (n = 48), subjects who contacted COVID-19 patients with no further infection (n = 46), and severe patients (n = 86). Using a novel compositional statistical algorithm (nearest balance) and the concept of bacterial co-occurrence clusters (coops), we compared microbiome compositions between the groups as well as with multiple categories of clinical parameters including: immunity, cardiovascular parameters and markers of endothelial dysfunction, and blood metabolites. Although a number of clinical indicators varied drastically across the three groups, no differences in microbiome features were identified between them at this follow-up point. However, there were multiple associations between the microbiome features and clinical data. Among the immunity parameters, the relative lymphocyte number was linked to a balance including 14 genera. Cardiovascular parameters were associated with up to four bacterial cooperatives. Intercellular adhesion molecule 1 was linked to a balance including ten genera and one cooperative. Among the blood biochemistry parameters, calcium was the only parameter associated with the microbiome via a balance of 16 genera. Our results suggest comparable recovery of the gut community structure in the post-COVID-19 period, independently of severity or infection status. The multiple identified associations of clinical analysis data with the microbiome provide hypotheses about the participation of specific taxa in regulating immunity and homeostasis of cardiovascular and other body systems in health, as well as their disruption in SARS-CoV-2 infections and other diseases.

Luminal and Tumor-Associated Gut Microbiome Features Linked to Precancerous Lesions Malignancy Risk: A Compositional Approach

Colorectal cancer is the third most commonly diagnosed cancer worldwide. Human gut microbiome plays important roles in protecting against it, as well as contributing to its onset and progression. Identification of specific bacterial taxa associated with early stages of colorectal cancer may help develop effective microbiome-based diagnostics. For precancerous lesions, links of their characteristics to luminal and tumor-associated microbiome composition are to be elucidated. Paired stool and tumor brush biopsy samples were collected from 50 patients with precancerous lesions and early forms of colon cancer; their microbial communities were profiled using high-throughput 16S rRNA sequencing. We showed that the microbiome differences between stool and biopsy samples can be to a high extent computationally corrected. Compositionality-aware statistical analysis of microbiome composition revealed its associations with the number of lesions, lesion type, location and malignization pathway. A major determinant of precancerous lesions malignancy risk-the number of lesions-was positively associated with the abundance of H2S-producing taxa. Our results contribute to the basis for developing early non-invasive colorectal cancer diagnostics via identifying microorganisms likely participating in early stages of cancer pathogenesis.

Three approaches to supervised learning for compositional data with pairwise logratios

Logratios between pairs of compositional parts (pairwise logratios) are the easiest to interpret in compositional data analysis, and include the well-known additive logratios as particular cases. When the number of parts is large (sometimes even larger than the number of cases), some form of logratio selection is needed. In this article, we present three alternative stepwise supervised learning methods to select the pairwise logratios that best explain a dependent variable in a generalized linear model, each geared for a specific problem. The first method features unrestricted search, where any pairwise logratio can be selected. This method has a complex interpretation if some pairs of parts in the logratios overlap, but it leads to the most accurate predictions. The second method restricts parts to occur only once, which makes the corresponding logratios intuitively interpretable. The third method uses additive logratios, so that K-1 selected logratios involve a K-part subcomposition. Our approach allows logratios or non-compositional covariates to be forced into the models based on theoretical knowledge, and various stopping criteria are available based on information measures or statistical significance with the Bonferroni correction. We present an application on a dataset from a study predicting Crohn's disease.